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To find something on the tube, you can zap through channels or you can zip onto this Web site. Zap2it.com provides local television listings and information about TV shows along with related entertainment industry news. The ad-supported Web site, which draws an audience of about 2.5 million people, offers local movie listings and information on DVD releases. Zap2it also licenses its content to third-party publishers, including Yahoo! and Microsoft's MSN. Launched in 2000 as a product of Tribune Media Services, Zap2it was formed through the merger of Web publishers MovieQuest and ultimatetv.com (its predecessor dating back to 1995).
[Description from Hoover's]

This site reaches over 1.6 million monthly people, of which 1.1 million (75%) are in the U.S.The typical visitor is insured by Allstate, plays games on bigfishgames.com, and drives a Ford.

Audience Interests

Definition

Top Interests: Quantcast Interests are content topics that represent interests of a property's users based on their browsing behavior. A user is "interested" in a topic if they visit sites that are similar to users who are known to have an interest in that topic. For example, if Joe is a sports enthusiast and visits sports sites exclusively, and Steve visits 90% of the same sites as Joe, Steve is likely to be a sports enthusiast.

Category Definitions: For a full list of categories and subcategories, as well as definitions, click here.

Top Sites: Quantcast Sites are the sites that the users of the profiled property visit in high proportions, relative to the overall internet population.

Interest Affinity Index: The Interest Affinity index measures how much more likely someone who visits this property is to be interested in a given topic compared to the average internet user. For example, an index of 11.0x for Computers & Technology means that users interested in Computer & Technology are 11 times more likely to have visited this property than the average internet user.

Site Affinity Index: The Site Affinity index measures how much more likely someone from this property is to visit the site listed compared to the average internet user. An index of 11.0x for JoesBlog.com means that users of this property are 11 times more likely to have visited JoesBlog.com than the average internet user.

Hierarchical View: This view shows all of the interest categories associated with this property, sorted by the index of the top-level interest category, for quick browsing of top-level interest categories. Use this view to quickly compare the Arts & Entertainment category to the Travel category.

List View: This view shows all of the interest categories and subcategories associated with this property, sorted by the index of the category, to give a more granular view for detailed browsing. Use this view to expand the Arts & Entertainment category into the Television and Music subcategories.

Monthly Overlap: This is the percent of the uniques (cookies or mobile devices) who visit the profiled property (e.g. Site A) that also visit the result property showing up in the same row in the table (e.g. Site B). The results also include the inverse - the percent of uniques who visit Site B and also visited Site A. For example, if JoesBlog.com is the top result for Site A with a Monthly Overlap of 2%, that means 2% of Site A's audience has visited JoesBlog.com.

Calculations

* The actual equation is slightly more complicated, with adjustments made to factor in confidence intervals and ensure higher reliability.

Information for Audience Interest reports is derived from a Quantcast cookie or mobile app that has integrated Quantcast's SDK. Because some browsers do not support 3rd-party cookies, these reports may represent a subset of your users' activity and not the overall composition of your site traffic.

In addition, some data in reports may be removed when thresholds are applied to prevent inferring the identity of an individual user (see our privacy policy). Finally, when looking at large data sets, we will sometimes use statistical sampling.

How to Use this Data

Audience interest reports help you understand the interests and lifestyles of a property's users, either by categorizing the interests users exhibit in their web browsing behavior, or by showing other properties they frequently visit.

Use this report to help you:

Tailor user acquisition efforts. Example.com is a tech site and is interested in growing their user base. Using the Top Sites report, they are able to identify other sites with similar audiences to focus their acquisition efforts.

Edit your content to appeal to valuable users. ExampleTVBlog.com uses the Top Interests report to find out that their audience is also 12x times more interested in Celebrity News than the average internet user. Now they can focus their TV reviews to include a celebrity news angle.

Provide data beyond demographics to support advertising sales efforts. Example.com is a news site that has used the segments feature to divide their site out into sections. They can then show advertisers that users of their "tech" section are big techies, even if the rest of the site is more of "general audience."

Demographic Index

Index represents how a site's audience compares to the online internet population as a whole. An index of 100 indicates a site's audience is at parity with the total internet population.

Affinity

The affinity numbers represent how likely a given visitor is to visit one of the listed sites or categories compared to the internet average. For example, an affinity number of 10.2 would say that a user on "X" website is 10.2 times more likely than the average internet visitor to visit the other site or category that is provided.

Directly Measured

This website is Quantified, and the data displayed here is directly measured by Quantcast.

Addicts

Addicts are the hardcore segment of a site's audience, who have 30 or more visits to that site in a month.

Regulars

Regulars refers to a segment of a site's audience that frequent a site more than once per month but not as much as addicts who frequent a site 30 or more times per month.

Passers-By

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Getting Quantified

Getting Quantified means you get free, directly measured and reliable audience and traffic data for the web properties you manage. Non-Quantified sites are still listed, but the data shown is estimated.

Request Site Get Quantified

You can send a request to this publisher asking them to join the Quantcast Publisher Program. The publisher
will be notified of your request via email. If and when the publisher decides to join, you will receive an email notification.

Request for Quantification Pending

You have sent a request to this site's owner to get quantified.
You will be notified via email when this site becomes quantified.

People from Sites & Syndicators

These percentages usually sum greater than 100% due to overlap in site and syndicated audiences.

Reading Demographic Graphs

1. Index

This compares audience composition of the site or mobile app to each platform population. The higher the index number, the more concentrated the property is in a particular demographic.

As an example, if a property indexes 100 for age 18-24, that means a given visitor to it is as likely to be 18-24 as any internet user chosen at random. An index of 200 means the visitor is twice as likely to be 18-24, 50 means half as likely, and so on.

Reading Demographic Graphs

This compares audience composition of the site or mobile app to each platform population. The higher the index number, the more concentrated the property is in a particular demographic.

As an example, if a property indexes 100 for age 18-24, that means a given visitor to it is as likely to be 18-24 as any internet user chosen at random. An index of 200 means the visitor is twice as likely to be 18-24, 50 means half as likely, and so on.

1. Segment refers to the demographic composition attribute.

2. Very High Indexes (over 200) are denoted with a plus symbol.

3. Internet Average is represented by the dotted vertical line.

4. Expand the data to see the numbers which make up the index calculation.

The expanded view shows the percentage composition, the Internet average and the multiple.

1. A Colored Bar indicates that a segment exceeds the Internet average, whereas a gray bar indicates the segment is below the Internet average. Internet average is represented by the dotted vertical line.

2. A Multiple is the percentage of the segment on this property divided by the average of the same segment on the entire Internet.

This chart breaks down the property's audience for a demographic. All the segments collectively equal 100%.

As an example, if a property indexes 100 for age 18-24, that means a given visitor to it is as likely to be 18-24 as any internet user chosen at random. An index of 200 means the visitor is twice as likely to be 18-24, 50 means half as likely, and so on.

1. The Top-Indexing Segment is shown in color.

Understanding User Retention

This graph examines user retention patterns for a mobile app, which tells the story of how much of app's user base continues to use the app after installation over time.

1. The x-axis is comprised of cohorts based on when users installed the app. For example, if we look at the column "+3 Days", this means that regardless of whether users installed the app a week ago or a month ago, what ratio of these users have returned within three days after installation.

2. The gray bars indicate the average retention rate across all days the app was downloaded.

3. The yellow line represents the average retention rate by period of all apps measured by Quantcast.

4. Install grouping details can be found by clicking on the down arrow.

In the expanded view, each row shows the retention patterns based on a point in time. Click on each row to compare that cohort against the average of all users installing the app.

1. The average day row shows the general retention rate for the entire app.

2. The highlighted row shows the retention rate compared against the average. In this example, 29% of users who installed the app one month ago returned at some point within two days, compared to the average of 35%.

Understanding Visit Frequency

This chart shows the number of return visits for unique users over the last 30 days.

1. Toggle between visit patterns of Logged In and Non Logged In users. In order to enable the toggle, the publisher must designate that the app has a logged in user base. The Logged In number represents the visit frequency of users that have logged in order to use this app.

Understanding Return Usage of Logged in Users

Digital brand offerings span across many device types and media channels. Quantcast allows brands to measure mobile web, online and app traffic. This feature allows a network property to demonstrate how logged in users migrate between these various platforms.

1. First Platform and First Cohort allow you to isolate a platform — for example mobile apps — and examine how users that start on mobile return over time to online, mobile web, apps or all of these platforms. The first cohort time range is for selecting a group of users you would like to track — for instance all users first seen within the course of a particular week an online ad campaign. Once defined, you can explore how this defined group of users returned, by platform, over time. A first-seen cohort may span up to 30 days.

2. Display Options allows you to choose available platforms to show return visits. More than one platform means that the logged in user returned on more than one platform (such as mobile apps and online) within the time period viewed. You can also select the amount of data points to review — 30, 60, or 90 days worth.

1. Bundled advertising inventory Media properties often bundle together advertising inventory across platforms into a single package for their clients. By showing that customers are continuously engaged across multiple platforms, networks can demonstrate what packaging options make the most sense in all of the contexts and formats these platforms provide.

2. Measure efforts to migrate an audience from one platform to another
This feature is a great way to isolate marketing efforts made to drive usage from one platform to another by looking at historical changes in platform adoption.

3. Compare return usages nuances between platforms Understand the nuances of usage pattern of customers on a particular platforms for product development decisions.